Personalized Privacy Preserving Updates to Anonymous Databases
Rajeshwari Suryawanshi, Parul Bhanarkar, Girish Saran Agarwal · 2013
Privacy of individual’s information in datasets is main concern in the present technological phase. Thus it is becoming an increasingly important issue in many data mining applications in various fields like medical research, hospital records maintenance, intelligence agencies etc. Many previous works has focused on generalization and suppression based anonymity which provides same amount of privacy preservation to all individuals. The paper focuses on devising private update techniques to database systems that supports notions of anonymity different than k-anonymity. Therefore the concept of personalized anonymity is used which performs the minimum generalization for satisfying everybody’s requirements, and thus, retains the largest amount of information from the microdata. Personalized Privacy is achieved by using SA (sensitive Attribute)-generalization to protect privacy of individual. In the paper, a method to perform updates on personalizes anonymity based database is proposed and its design view is explained.